Java NLU Tools

Java tools to do natural language processing like NER and intent classification on short sentences.
These tools provide an abstraction layer to MALLET: A Machine Learning for Language Toolkit (Common Public License) and Apache OpenNLP (Apache License Version 2.0). Stanford CoreNLP is supported as well but due to their GPL license it is implemented as plugin.

How to

All training data can be stored in a common, easy to read file with the following format:

How is the weather? --- O O O O --- WEATHER
How's the weather in Berlin? --- O O O O LOCATION --- WEATHER
I need a taxi --- O O O O --- TAXI_SERVICE
I need a cap to go to Pier 39 --- O O O O O O O LOCATION LOCATION --- TAXI_SERVICE
...

Each line starts with a sentence in raw format followed by labels that are used to extract named entities and an intent connected to the sentence. Each part is separated by 3 dashes with space " --- ". The default label for unnamed words (tokens) is "O", labels can be chosen as you like. The more sentences you have the better, but 15 per intent and named-entity should be fine for testing.

To start training your model first choose your toolkit and extract the data to the required format:

//Import training data from compact custom format
Collection<CompactDataEntry> trainData = CustomDataHandler.importCompactData(compactTrainDataFile);

//Declare a tokenizer for our model
Tokenizer tokenizer = new RealLifeChatTokenizer("", "", "");

//Store train data for OpenNLP intent classification
CompactDataHandler cdh = new OpenNlpDataHandler();
List<String> trainDataLines = cdh.importTrainDataIntent(trainData, tokenizer, null);
CustomDataHandler.writeTrainData(trainFile, trainDataLines);

Then you can start training:

//Start training with intent trainer
IntentTrainer trainer = new OpenNlpIntentTrainer(null, trainFileBase, modelFileBase, languageCode);
trainer.train();

To test your model load it in a new classifier and call it like this:

IntentClassifier ic = new OpenNlpIntentClassifier(modelFileBase, tokenizer, languageCode);
Collection<IntentEntry> intentResults = ic.analyzeSentence(sentence);
IntentEntry bestResult = IntentEntry.getBestIntent(intentResults);
String bestIntent = bestResult.getIntent();
double bestCertainty = bestResult.getCertainty();

Check out the examples for each toolkit to get a complete overview of the export-train-test procedure.